A method for contactless palm ROI extraction

Palmprint can be extracted from a hand using a low-cost webcam in a contactless manner. Using a webcam makes the enrollment process fast and convenient for users. Being contactless solve the hygiene issue and avoid copying the latent prints from sensor's surface. However, a number of challenges arise in such environment; geometric transformations, the existence of finger rings, hand accessories, and other false objects. This paper proposes a palm ROI extraction method that is robust to these challenges. The method is based on blob analysis, morphological and geometrical operations without a need to pre-train or parameter adjustment. It's tested on three available hand databases that cover these challenges; namely, Sfax, IITD and PolyU 3D/2D. The palm ROI is considered to be wrongly extracted if it contains part of the background. The method achieves an extraction error of 0%, 0.27% and 0.26% for the three DBs, respectively. Applying a massive rotation and scaling tests leads to a minor increase in the extraction errors by 0.24%, 0.35% and 0.84% for the three DBs, respectively.